Envelope spectrum knowledge-guided domain invariant representation learning strategy for intelligent fault diagnosis of bearing

Abstract

Deep learning has significantly advanced bearing fault diagnosis. Traditional models rely on the assumption of independent and identically distributed, which is frequently violated due to variations in rotational speeds and loads during bearing fault diagnosis. The fault diagnosis of the bearing based on representation learning lacks the consideration of spectrum knowledge and representation diversity under multiple working conditions. Therefore, this study presents a domain-invariant representation learning strategy (DIRLs) for diagnosing bearing faults across differing working conditions. DIRLs, by leveraging envelope spectrum knowledge distillation, captures the Fourier characteristics as domain-invariant features and secures robust health state representations by aligning high-order statistics of the samples under different working conditions. Moreover, an innovative loss function, which maximizes the two-paradigm metric of the health state representation, is designed to enrich representation diversity. Experimental results demonstrate an average AUC improvement of 28.6 % on the Paderborn-bearing dataset and an overall diagnostic accuracy of 88.7 % on a private bearing dataset, validating the effectiveness of the proposed method.